A system for image retrieval is described in an article by Y. Rui, et al, entitled “Relevance Feedback Techniques in Interactive Content-based Image Retrieval,” in Proc. IS&T and SPIE Storage and Retrieval of Image and Video Databases VI, San Jose, Calif., USA, January 1998, pages 25-36, hereinafter referred to as Ref. 1. In the relevance feedback approach the user provides feedback on a retrieved image to the system. First, the system is arranged to retrieve images from a collection of images, based on a query image, by comparing predetermined features of the query image to the respective features of each image of the collection of images. Second, the user ranks retrieved images according to their resemblance to the query image in the view of the user. This ranking is called “relevance feedback”. The relevance feedback is then used to compute optimal weights of features used for identifying images for retrieval, and hence to determine relevant features. The features and their weights define a new similarity measure, suitable for the query image or for a family of similar query images. The new similarity measure may be used for retrieving an image from the collection of images, based on a query image of the family of query images.